Voltage-dependent movement of a sodium channel S4 segment was examined by cysteine scanning mutagenesis and testing accessibility of the residues to hydrophilic cysteine-modifying reagents. These experiments indicate that 2 charged S4 residues move completely from an internally accessible to an externally accessible location in response to depolarization by passage through a short "channel" in the protein. The energetic problems of S4 movement have thus been solved in the same way that may ion channels achieve highly selective and rapid ion permeation through an open pore, by restricting the contact region between the permion and its channel.

No abstract

The mutation R1448C substitutes a cysteine for the outermost arginine in the fourth transmembrane segment (S4) of domain 4 in skeletal muscle sodium channels. We tested the accessibility of this cysteine residue to hydrophilic methanethiosulfonate reagents applied to the extracellular surface of cells expressing these mutant channels. The reagents irreversibly increase the rate of inactivation of R1448C, but not wild-type, channels. Cysteine modification is voltage dependent, as if depolarization extends this residue into the extracellular space. The rate of cysteine modification increases with depolarization and has the voltage dependence and kinetics expected for the movement of a voltage sensor controlling channel gating.

Mutations in the skeletal muscle voltage-

The second and third basic residues of the S4 segment of domain 4 (D4:R2 and D4:R3) of the human skeletal muscle Na+ channel are known to be translocated from a cytoplasmic to an extracellular position during depolarization. Accessibilities of individual S4 residues were assayed by alteration of inactivation kinetics during modification of cysteine mutants by hydrophilic methanethiosulfonate reagents. The voltage dependences of the reaction rates are identical for extracellular application of cationic methanethiosulfonate-ethyltrimethylammonium (MTSET) and anionic methanethiosulfonate-ethylsulfonate (MTSES), suggesting that D4:R3C is situated outside the membrane electric field at depolarized voltages. The absolute rate of R3C modification is 281-fold greater for MTSET than for MTSES, however, suggesting that at depolarized voltages this S4 thiol resides in a negatively charged hydrophilic crevice. The two hydrophobic residues between D4:R2C and D4:R3C in the primary sequence (L1452 and A1453) are not externally exposed at any voltage. An alpha-helical representation of D4/S4 shows that the basic residues D4:R2 and D4:R3 are on the face opposite that of L1452 and A1453. We propose that in the depolarized conformation, the hydrophobic face of this portion of D4/S4 remains in contact with a hydrophobic region of the extracellular vestibule of the S4 channel.

Single-cell RNA-seq technologies require library preparation prior to sequencing. Here, we present the first report to compare the cheaper BGISEQ-500 platform to the Illumina HiSeq platform for scRNA-seq. We generate a resource of 468 single cells and 1297 matched single cDNA samples, performing SMARTer and Smart-seq2 protocols on two cell lines with RNA spike-ins. We sequence these libraries on both platforms using single- and paired-end reads. The platforms have comparable sensitivity and accuracy in terms of quantification of gene expression, and low technical variability. Our study provides a standardized scRNA-seq resource to benchmark new scRNA-seq library preparation protocols and sequencing platforms. Electronic supplementary material The online version of this article (10.1186/s13059-019-1676-5) contains supplementary material, which is available to authorized users.

No abstract

Predicting peptide binding affinity with human leukocyte antigen (HLA) is a crucial step in developing powerful antitumor vaccine for cancer immunotherapy. Currently available methods work quite well in predicting peptide binding affinity with HLA alleles such as HLA-A*0201, HLA-A*0101, and HLA-B*0702 in terms of sensitivity and specificity. However, quite a few types of HLA alleles that are present in the majority of human populations including HLA-A*0202, HLA-A*0203, HLA-A*6802, HLA-B*5101, HLA-B*5301, HLA-B*5401, and HLA-B*5701 still cannot be predicted with satisfactory accuracy using currently available methods. Furthermore, currently the most popularly used methods for predicting peptide binding affinity are inefficient in identifying neoantigens from a large quantity of whole genome and transcriptome sequencing data. Here we present a Position Specific Scoring Matrix (PSSM)-based software called PSSMHCpan to accurately and efficiently predict peptide binding affinity with a broad coverage of HLA class I alleles. We evaluated the performance of PSSMHCpan by analyzing 10-fold cross-validation on a training database containing 87 HLA alleles and obtained an average area under receiver operating characteristic curve (AUC) of 0.94 and accuracy (ACC) of 0.85. In an independent dataset (Peptide Database of Cancer Immunity) evaluation, PSSMHCpan is substantially better than the popularly used NetMHC-4.0, NetMHCpan-3.0, PickPocket, Nebula, and SMM with a sensitivity of 0.90, as compared to 0.74, 0.81, 0.77, 0.24, and 0.79. In addition, PSSMHCpan is more than 197 times faster than NetMHC-4.0, NetMHCpan-3.0, PickPocket, sNebula, and SMM when predicting neoantigens from 661 263 peptides from a breast tumor sample. Finally, we built a neoantigen prediction pipeline and identified 117 017 neoantigens from 467 cancer samples of various cancers from TCGA. PSSMHCpan is superior to the currently available methods in predicting peptide binding affinity with a broad coverage of HLA class I alleles.